Overview

Brought to you by YData

Dataset statistics

Number of variables45
Number of observations240000
Missing cells8890408
Missing cells (%)82.3%
Duplicate rows612
Duplicate rows (%)0.3%
Total size in memory92.3 MiB
Average record size in memory403.2 B

Variable types

DateTime1
Text2
Categorical22
Numeric12
Unsupported8

Alerts

placement_support_banner_tab_clicked has constant value "1.0" Constant
programme_curriculum has constant value "1.0" Constant
programme_faculty has constant value "1.0" Constant
request_callback_on_instant_customer_support_cta_clicked has constant value "1.0" Constant
Dataset has 612 (0.3%) duplicate rowsDuplicates
app_complete_flag is highly overall correlated with call_us_button_clicked and 3 other fieldsHigh correlation
call_us_button_clicked is highly overall correlated with app_complete_flag and 7 other fieldsHigh correlation
career_assistance is highly overall correlated with app_complete_flag and 12 other fieldsHigh correlation
career_impact is highly overall correlated with career_assistance and 8 other fieldsHigh correlation
careers is highly overall correlated with career_assistance and 3 other fieldsHigh correlation
companies is highly overall correlated with app_complete_flag and 15 other fieldsHigh correlation
download_button_clicked is highly overall correlated with call_us_button_clicked and 12 other fieldsHigh correlation
emi_partner_click is highly overall correlated with call_us_button_clicked and 14 other fieldsHigh correlation
emi_plans_clicked is highly overall correlated with career_assistance and 12 other fieldsHigh correlation
fee_component_click is highly overall correlated with download_button_clicked and 6 other fieldsHigh correlation
first_platform_c is highly overall correlated with call_us_button_clicked and 3 other fieldsHigh correlation
first_utm_medium_c is highly overall correlated with call_us_button_clicked and 5 other fieldsHigh correlation
hiring_partners is highly overall correlated with career_assistance and 5 other fieldsHigh correlation
live_chat_button_clicked is highly overall correlated with referred_lead and 4 other fieldsHigh correlation
placement_support is highly overall correlated with download_button_clicked and 6 other fieldsHigh correlation
referred_lead is highly overall correlated with call_us_button_clicked and 10 other fieldsHigh correlation
shorts_entry_click is highly overall correlated with emi_partner_click and 1 other fieldsHigh correlation
social_referral_click is highly overall correlated with first_utm_medium_c and 1 other fieldsHigh correlation
specialisation_tab_clicked is highly overall correlated with career_assistance and 9 other fieldsHigh correlation
specializations is highly overall correlated with app_complete_flag and 9 other fieldsHigh correlation
specilization_click is highly overall correlated with download_button_clicked and 5 other fieldsHigh correlation
syllabus is highly overall correlated with career_assistance and 4 other fieldsHigh correlation
syllabus_expand is highly overall correlated with call_us_button_clicked and 13 other fieldsHigh correlation
syllabus_submodule_expand is highly overall correlated with career_assistance and 11 other fieldsHigh correlation
tab_career_assistance is highly overall correlated with career_assistance and 9 other fieldsHigh correlation
tab_job_opportunities is highly overall correlated with career_assistance and 10 other fieldsHigh correlation
tab_student_support is highly overall correlated with career_assistance and 6 other fieldsHigh correlation
total_leads_droppped is highly overall correlated with call_us_button_clicked and 10 other fieldsHigh correlation
view_programs_page is highly overall correlated with emi_partner_click and 3 other fieldsHigh correlation
whatsapp_chat_click is highly overall correlated with download_button_clicked and 10 other fieldsHigh correlation
referred_lead is highly imbalanced (79.4%) Imbalance
city_mapped has 9403 (3.9%) missing values Missing
1_on_1_industry_mentorship has 240000 (100.0%) missing values Missing
call_us_button_clicked has 239998 (> 99.9%) missing values Missing
career_assistance has 239996 (> 99.9%) missing values Missing
career_coach has 240000 (100.0%) missing values Missing
career_impact has 239995 (> 99.9%) missing values Missing
careers has 239980 (> 99.9%) missing values Missing
chat_clicked has 240000 (100.0%) missing values Missing
companies has 239998 (> 99.9%) missing values Missing
download_button_clicked has 239893 (> 99.9%) missing values Missing
download_syllabus has 240000 (100.0%) missing values Missing
emi_partner_click has 239921 (> 99.9%) missing values Missing
emi_plans_clicked has 239939 (> 99.9%) missing values Missing
fee_component_click has 239995 (> 99.9%) missing values Missing
hiring_partners has 239991 (> 99.9%) missing values Missing
homepage_upgrad_support_number_clicked has 240000 (100.0%) missing values Missing
industry_projects_case_studies has 240000 (100.0%) missing values Missing
live_chat_button_clicked has 239982 (> 99.9%) missing values Missing
payment_amount_toggle_mover has 240000 (100.0%) missing values Missing
placement_support has 239997 (> 99.9%) missing values Missing
placement_support_banner_tab_clicked has 239999 (> 99.9%) missing values Missing
program_structure has 240000 (100.0%) missing values Missing
programme_curriculum has 239999 (> 99.9%) missing values Missing
programme_faculty has 239999 (> 99.9%) missing values Missing
request_callback_on_instant_customer_support_cta_clicked has 239998 (> 99.9%) missing values Missing
shorts_entry_click has 239987 (> 99.9%) missing values Missing
social_referral_click has 239995 (> 99.9%) missing values Missing
specialisation_tab_clicked has 239956 (> 99.9%) missing values Missing
specializations has 239996 (> 99.9%) missing values Missing
specilization_click has 239993 (> 99.9%) missing values Missing
syllabus has 239971 (> 99.9%) missing values Missing
syllabus_expand has 239906 (> 99.9%) missing values Missing
syllabus_submodule_expand has 239958 (> 99.9%) missing values Missing
tab_career_assistance has 239975 (> 99.9%) missing values Missing
tab_job_opportunities has 239981 (> 99.9%) missing values Missing
tab_student_support has 239992 (> 99.9%) missing values Missing
view_programs_page has 239988 (> 99.9%) missing values Missing
whatsapp_chat_click has 239975 (> 99.9%) missing values Missing
call_us_button_clicked is uniformly distributed Uniform
companies is uniformly distributed Uniform
app_complete_flag is uniformly distributed Uniform
1_on_1_industry_mentorship is an unsupported type, check if it needs cleaning or further analysis Unsupported
career_coach is an unsupported type, check if it needs cleaning or further analysis Unsupported
chat_clicked is an unsupported type, check if it needs cleaning or further analysis Unsupported
download_syllabus is an unsupported type, check if it needs cleaning or further analysis Unsupported
homepage_upgrad_support_number_clicked is an unsupported type, check if it needs cleaning or further analysis Unsupported
industry_projects_case_studies is an unsupported type, check if it needs cleaning or further analysis Unsupported
payment_amount_toggle_mover is an unsupported type, check if it needs cleaning or further analysis Unsupported
program_structure is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-04-24 03:59:41.892889
Analysis finished2025-04-24 03:59:53.899589
Duration12.01 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct234753
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size11.7 MiB
Minimum2021-07-01 00:08:15
Maximum2022-02-14 08:45:40
Invalid dates0
Invalid dates (%)0.0%
2025-04-24T09:29:53.928537image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:53.980034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

city_mapped
Text

Missing 

Distinct4597
Distinct (%)2.0%
Missing9403
Missing (%)3.9%
Memory size11.7 MiB
2025-04-24T09:29:54.082198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length61
Median length47
Mean length6.734281
Min length1

Characters and Unicode

Total characters1552905
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2427 ?
Unique (%)1.1%

Sample

1st rowmumbai
2nd rowncr
3rd rowmeerut
4th rowncr
5th rowahmedabad
ValueCountFrequency (%)
ncr 35265
 
15.0%
mumbai 22193
 
9.4%
bengaluru 19531
 
8.3%
hyderabad 14376
 
6.1%
pune 14021
 
6.0%
chennai 10121
 
4.3%
kolkata 8349
 
3.6%
lucknow 7073
 
3.0%
ahmedabad 6101
 
2.6%
jaipur 4645
 
2.0%
Other values (4688) 93479
39.8%
2025-04-24T09:29:54.230944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 260306
16.8%
n 143332
 
9.2%
r 140618
 
9.1%
u 127886
 
8.2%
e 89195
 
5.7%
b 83535
 
5.4%
i 81499
 
5.2%
d 73791
 
4.8%
m 72086
 
4.6%
h 71745
 
4.6%
Other values (17) 408912
26.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1548348
99.7%
Space Separator 4557
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 260306
16.8%
n 143332
 
9.3%
r 140618
 
9.1%
u 127886
 
8.3%
e 89195
 
5.8%
b 83535
 
5.4%
i 81499
 
5.3%
d 73791
 
4.8%
m 72086
 
4.7%
h 71745
 
4.6%
Other values (16) 404355
26.1%
Space Separator
ValueCountFrequency (%)
4557
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1548348
99.7%
Common 4557
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 260306
16.8%
n 143332
 
9.3%
r 140618
 
9.1%
u 127886
 
8.3%
e 89195
 
5.8%
b 83535
 
5.4%
i 81499
 
5.3%
d 73791
 
4.8%
m 72086
 
4.7%
h 71745
 
4.6%
Other values (16) 404355
26.1%
Common
ValueCountFrequency (%)
4557
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1552905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 260306
16.8%
n 143332
 
9.2%
r 140618
 
9.1%
u 127886
 
8.2%
e 89195
 
5.7%
b 83535
 
5.4%
i 81499
 
5.2%
d 73791
 
4.8%
m 72086
 
4.6%
h 71745
 
4.6%
Other values (17) 408912
26.3%

first_platform_c
Categorical

High correlation 

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.7 MiB
Level0
84532 
Level3
41028 
Level7
38267 
Level1
20832 
Level2
13284 
Other values (44)
42057 

Length

Max length7
Median length6
Mean length6.063625
Min length6

Characters and Unicode

Total characters1455270
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowLevel0
2nd rowLevel1
3rd rowLevel0
4th rowLevel1
5th rowLevel2

Common Values

ValueCountFrequency (%)
Level0 84532
35.2%
Level3 41028
17.1%
Level7 38267
15.9%
Level1 20832
 
8.7%
Level2 13284
 
5.5%
Level8 10838
 
4.5%
Level5 9768
 
4.1%
Level4 5514
 
2.3%
Level16 3790
 
1.6%
Level10 3288
 
1.4%
Other values (39) 8859
 
3.7%

Length

2025-04-24T09:29:54.274426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
level0 84532
35.2%
level3 41028
17.1%
level7 38267
15.9%
level1 20832
 
8.7%
level2 13284
 
5.5%
level8 10838
 
4.5%
level5 9768
 
4.1%
level4 5514
 
2.3%
level16 3790
 
1.6%
level10 3288
 
1.4%
Other values (39) 8859
 
3.7%

Most occurring characters

ValueCountFrequency (%)
e 480000
33.0%
L 240000
16.5%
v 240000
16.5%
l 240000
16.5%
0 87917
 
6.0%
3 41784
 
2.9%
7 38362
 
2.6%
1 36380
 
2.5%
2 16844
 
1.2%
8 12972
 
0.9%
Other values (4) 21011
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 960000
66.0%
Decimal Number 255270
 
17.5%
Uppercase Letter 240000
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 87917
34.4%
3 41784
16.4%
7 38362
15.0%
1 36380
14.3%
2 16844
 
6.6%
8 12972
 
5.1%
5 9891
 
3.9%
4 6309
 
2.5%
6 4526
 
1.8%
9 285
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 480000
50.0%
v 240000
25.0%
l 240000
25.0%
Uppercase Letter
ValueCountFrequency (%)
L 240000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1200000
82.5%
Common 255270
 
17.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 87917
34.4%
3 41784
16.4%
7 38362
15.0%
1 36380
14.3%
2 16844
 
6.6%
8 12972
 
5.1%
5 9891
 
3.9%
4 6309
 
2.5%
6 4526
 
1.8%
9 285
 
0.1%
Latin
ValueCountFrequency (%)
e 480000
40.0%
L 240000
20.0%
v 240000
20.0%
l 240000
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1455270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 480000
33.0%
L 240000
16.5%
v 240000
16.5%
l 240000
16.5%
0 87917
 
6.0%
3 41784
 
2.9%
7 38362
 
2.6%
1 36380
 
2.5%
2 16844
 
1.2%
8 12972
 
0.9%
Other values (4) 21011
 
1.4%

first_utm_medium_c
Categorical

High correlation 

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.7 MiB
Level0
54338 
Level2
27029 
Level6
23912 
Level3
20009 
Level4
18509 
Other values (40)
96203 

Length

Max length7
Median length6
Mean length6.2573958
Min length6

Characters and Unicode

Total characters1501775
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLevel0
2nd rowLevel1
3rd rowLevel2
4th rowLevel3
5th rowLevel2

Common Values

ValueCountFrequency (%)
Level0 54338
22.6%
Level2 27029
11.3%
Level6 23912
10.0%
Level3 20009
 
8.3%
Level4 18509
 
7.7%
Level9 15595
 
6.5%
Level11 9628
 
4.0%
Level5 9097
 
3.8%
Level8 7381
 
3.1%
Level20 4187
 
1.7%
Other values (35) 50315
21.0%

Length

2025-04-24T09:29:54.313808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
level0 54338
22.6%
level2 27029
11.3%
level6 23912
10.0%
level3 20009
 
8.3%
level4 18509
 
7.7%
level9 15595
 
6.5%
level11 9628
 
4.0%
level5 9097
 
3.8%
level8 7381
 
3.1%
level20 4187
 
1.7%
Other values (35) 50315
21.0%

Most occurring characters

ValueCountFrequency (%)
e 480000
32.0%
L 240000
16.0%
v 240000
16.0%
l 240000
16.0%
0 66127
 
4.4%
3 45361
 
3.0%
2 43948
 
2.9%
1 42378
 
2.8%
6 31235
 
2.1%
4 26575
 
1.8%
Other values (4) 46151
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 960000
63.9%
Decimal Number 301775
 
20.1%
Uppercase Letter 240000
 
16.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66127
21.9%
3 45361
15.0%
2 43948
14.6%
1 42378
14.0%
6 31235
10.4%
4 26575
8.8%
9 19569
 
6.5%
5 14132
 
4.7%
8 9223
 
3.1%
7 3227
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
e 480000
50.0%
v 240000
25.0%
l 240000
25.0%
Uppercase Letter
ValueCountFrequency (%)
L 240000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1200000
79.9%
Common 301775
 
20.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66127
21.9%
3 45361
15.0%
2 43948
14.6%
1 42378
14.0%
6 31235
10.4%
4 26575
8.8%
9 19569
 
6.5%
5 14132
 
4.7%
8 9223
 
3.1%
7 3227
 
1.1%
Latin
ValueCountFrequency (%)
e 480000
40.0%
L 240000
20.0%
v 240000
20.0%
l 240000
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1501775
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 480000
32.0%
L 240000
16.0%
v 240000
16.0%
l 240000
16.0%
0 66127
 
4.4%
3 45361
 
3.0%
2 43948
 
2.9%
1 42378
 
2.8%
6 31235
 
2.1%
4 26575
 
1.8%
Other values (4) 46151
 
3.1%
Distinct142
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.7 MiB
2025-04-24T09:29:54.389884image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.1574625
Min length6

Characters and Unicode

Total characters1477791
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)< 0.1%

Sample

1st rowLevel0
2nd rowLevel1
3rd rowLevel2
4th rowLevel3
5th rowLevel2
ValueCountFrequency (%)
level2 83146
34.6%
level0 56709
23.6%
level7 18719
 
7.8%
level4 16133
 
6.7%
level6 15728
 
6.6%
level16 11081
 
4.6%
level5 5517
 
2.3%
level14 4856
 
2.0%
level11 4415
 
1.8%
level12 4154
 
1.7%
Other values (132) 19542
 
8.1%
2025-04-24T09:29:54.509302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 480000
32.5%
L 240000
16.2%
v 240000
16.2%
l 240000
16.2%
2 94419
 
6.4%
0 57225
 
3.9%
1 37251
 
2.5%
6 27517
 
1.9%
4 21857
 
1.5%
7 19210
 
1.3%
Other values (4) 20312
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 960000
65.0%
Decimal Number 277791
 
18.8%
Uppercase Letter 240000
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 94419
34.0%
0 57225
20.6%
1 37251
 
13.4%
6 27517
 
9.9%
4 21857
 
7.9%
7 19210
 
6.9%
5 8368
 
3.0%
9 4702
 
1.7%
3 4025
 
1.4%
8 3217
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
e 480000
50.0%
v 240000
25.0%
l 240000
25.0%
Uppercase Letter
ValueCountFrequency (%)
L 240000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1200000
81.2%
Common 277791
 
18.8%

Most frequent character per script

Common
ValueCountFrequency (%)
2 94419
34.0%
0 57225
20.6%
1 37251
 
13.4%
6 27517
 
9.9%
4 21857
 
7.9%
7 19210
 
6.9%
5 8368
 
3.0%
9 4702
 
1.7%
3 4025
 
1.4%
8 3217
 
1.2%
Latin
ValueCountFrequency (%)
e 480000
40.0%
L 240000
20.0%
v 240000
20.0%
l 240000
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1477791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 480000
32.5%
L 240000
16.2%
v 240000
16.2%
l 240000
16.2%
2 94419
 
6.4%
0 57225
 
3.9%
1 37251
 
2.5%
6 27517
 
1.9%
4 21857
 
1.5%
7 19210
 
1.3%
Other values (4) 20312
 
1.4%

total_leads_droppped
Real number (ℝ)

High correlation 

Distinct57
Distinct (%)< 0.1%
Missing826
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.6508107
Minimum1
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 MiB
2025-04-24T09:29:54.554009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum89
Range88
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3652276
Coefficient of variation (CV)0.82700435
Kurtosis406.08234
Mean1.6508107
Median Absolute Deviation (MAD)0
Skewness12.563592
Sum394831
Variance1.8638465
MonotonicityNot monotonic
2025-04-24T09:29:54.598253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 145967
60.8%
2 60640
25.3%
3 19854
 
8.3%
4 6881
 
2.9%
5 2817
 
1.2%
6 1267
 
0.5%
7 631
 
0.3%
8 361
 
0.2%
9 203
 
0.1%
10 120
 
0.1%
Other values (47) 433
 
0.2%
(Missing) 826
 
0.3%
ValueCountFrequency (%)
1 145967
60.8%
2 60640
25.3%
3 19854
 
8.3%
4 6881
 
2.9%
5 2817
 
1.2%
6 1267
 
0.5%
7 631
 
0.3%
8 361
 
0.2%
9 203
 
0.1%
10 120
 
0.1%
ValueCountFrequency (%)
89 2
< 0.1%
71 1
< 0.1%
60 1
< 0.1%
58 1
< 0.1%
54 1
< 0.1%
53 1
< 0.1%
52 2
< 0.1%
51 2
< 0.1%
50 2
< 0.1%
48 2
< 0.1%

referred_lead
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing826
Missing (%)0.3%
Memory size11.7 MiB
0.0
231430 
1.0
 
7744

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters717522
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 231430
96.4%
1.0 7744
 
3.2%
(Missing) 826
 
0.3%

Length

2025-04-24T09:29:54.637523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:54.666397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 231430
96.8%
1.0 7744
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 470604
65.6%
. 239174
33.3%
1 7744
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 478348
66.7%
Other Punctuation 239174
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 470604
98.4%
1 7744
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 239174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 717522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 470604
65.6%
. 239174
33.3%
1 7744
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 717522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 470604
65.6%
. 239174
33.3%
1 7744
 
1.1%

1_on_1_industry_mentorship
Unsupported

Missing  Rejected  Unsupported 

Missing240000
Missing (%)100.0%
Memory size11.7 MiB

call_us_button_clicked
Categorical

High correlation  Missing  Uniform 

Distinct2
Distinct (%)100.0%
Missing239998
Missing (%)> 99.9%
Memory size11.7 MiB
2.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row2.0
2nd row1.0

Common Values

ValueCountFrequency (%)
2.0 1
 
< 0.1%
1.0 1
 
< 0.1%
(Missing) 239998
> 99.9%

Length

2025-04-24T09:29:54.696489image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:54.727688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1
50.0%
1.0 1
50.0%

Most occurring characters

ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
2 1
16.7%
1 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2
50.0%
2 1
25.0%
1 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
2 1
16.7%
1 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
2 1
16.7%
1 1
16.7%

career_assistance
Categorical

High correlation  Missing 

Distinct3
Distinct (%)75.0%
Missing239996
Missing (%)> 99.9%
Memory size11.7 MiB
1.0
2.0
3.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
2.0 1
 
< 0.1%
3.0 1
 
< 0.1%
(Missing) 239996
> 99.9%

Length

2025-04-24T09:29:54.759825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:54.791872image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
50.0%
2.0 1
25.0%
3.0 1
25.0%

Most occurring characters

ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 2
16.7%
2 1
 
8.3%
3 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8
66.7%
Other Punctuation 4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4
50.0%
1 2
25.0%
2 1
 
12.5%
3 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 2
16.7%
2 1
 
8.3%
3 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 2
16.7%
2 1
 
8.3%
3 1
 
8.3%

career_coach
Unsupported

Missing  Rejected  Unsupported 

Missing240000
Missing (%)100.0%
Memory size11.7 MiB

career_impact
Categorical

High correlation  Missing 

Distinct3
Distinct (%)60.0%
Missing239995
Missing (%)> 99.9%
Memory size11.7 MiB
1.0
3.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)20.0%

Sample

1st row1.0
2nd row1.0
3rd row3.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
3.0 2
 
< 0.1%
2.0 1
 
< 0.1%
(Missing) 239995
> 99.9%

Length

2025-04-24T09:29:54.826464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:54.856883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
40.0%
3.0 2
40.0%
2.0 1
20.0%

Most occurring characters

ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
1 2
 
13.3%
3 2
 
13.3%
2 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5
50.0%
1 2
 
20.0%
3 2
 
20.0%
2 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
1 2
 
13.3%
3 2
 
13.3%
2 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
1 2
 
13.3%
3 2
 
13.3%
2 1
 
6.7%

careers
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)40.0%
Missing239980
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean4
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 MiB
2025-04-24T09:29:54.885252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33.25
95-th percentile11.9
Maximum29
Range28
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation6.4969629
Coefficient of variation (CV)1.6242407
Kurtosis12.454078
Mean4
Median Absolute Deviation (MAD)1
Skewness3.371356
Sum80
Variance42.210526
MonotonicityNot monotonic
2025-04-24T09:29:54.919209image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 9
 
< 0.1%
2 5
 
< 0.1%
3 1
 
< 0.1%
29 1
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
(Missing) 239980
> 99.9%
ValueCountFrequency (%)
1 9
< 0.1%
2 5
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
29 1
 
< 0.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
2 5
< 0.1%
1 9
< 0.1%

chat_clicked
Unsupported

Missing  Rejected  Unsupported 

Missing240000
Missing (%)100.0%
Memory size11.7 MiB

companies
Categorical

High correlation  Missing  Uniform 

Distinct2
Distinct (%)100.0%
Missing239998
Missing (%)> 99.9%
Memory size11.7 MiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row1.0
2nd row2.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
2.0 1
 
< 0.1%
(Missing) 239998
> 99.9%

Length

2025-04-24T09:29:54.955569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:54.986336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
50.0%
2.0 1
50.0%

Most occurring characters

ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
1 1
16.7%
2 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2
50.0%
1 1
25.0%
2 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
1 1
16.7%
2 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2
33.3%
0 2
33.3%
1 1
16.7%
2 1
16.7%

download_button_clicked
Real number (ℝ)

High correlation  Missing 

Distinct31
Distinct (%)29.0%
Missing239893
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean10.551402
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 MiB
2025-04-24T09:29:55.021566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q37.5
95-th percentile46.8
Maximum98
Range97
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation18.655888
Coefficient of variation (CV)1.7680956
Kurtosis9.0514248
Mean10.551402
Median Absolute Deviation (MAD)2
Skewness2.8735503
Sum1129
Variance348.04214
MonotonicityNot monotonic
2025-04-24T09:29:55.063439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 31
 
< 0.1%
2 22
 
< 0.1%
3 10
 
< 0.1%
4 8
 
< 0.1%
7 3
 
< 0.1%
5 3
 
< 0.1%
6 3
 
< 0.1%
8 2
 
< 0.1%
36 2
 
< 0.1%
57 2
 
< 0.1%
Other values (21) 21
 
< 0.1%
(Missing) 239893
> 99.9%
ValueCountFrequency (%)
1 31
< 0.1%
2 22
< 0.1%
3 10
 
< 0.1%
4 8
 
< 0.1%
5 3
 
< 0.1%
6 3
 
< 0.1%
7 3
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
98 1
< 0.1%
97 1
< 0.1%
75 1
< 0.1%
57 2
< 0.1%
48 1
< 0.1%
44 1
< 0.1%
40 1
< 0.1%
36 2
< 0.1%
34 1
< 0.1%
33 1
< 0.1%

download_syllabus
Unsupported

Missing  Rejected  Unsupported 

Missing240000
Missing (%)100.0%
Memory size11.7 MiB

emi_partner_click
Real number (ℝ)

High correlation  Missing 

Distinct39
Distinct (%)49.4%
Missing239921
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean27.860759
Minimum1
Maximum319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 MiB
2025-04-24T09:29:55.102738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.9
Q14
median9
Q327
95-th percentile122.1
Maximum319
Range318
Interquartile range (IQR)23

Descriptive statistics

Standard deviation48.498145
Coefficient of variation (CV)1.7407331
Kurtosis16.75135
Mean27.860759
Median Absolute Deviation (MAD)7
Skewness3.5880217
Sum2201
Variance2352.0701
MonotonicityNot monotonic
2025-04-24T09:29:55.144158image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2 9
 
< 0.1%
6 7
 
< 0.1%
5 5
 
< 0.1%
4 5
 
< 0.1%
11 5
 
< 0.1%
8 4
 
< 0.1%
1 4
 
< 0.1%
3 3
 
< 0.1%
13 3
 
< 0.1%
17 2
 
< 0.1%
Other values (29) 32
 
< 0.1%
(Missing) 239921
> 99.9%
ValueCountFrequency (%)
1 4
< 0.1%
2 9
< 0.1%
3 3
 
< 0.1%
4 5
< 0.1%
5 5
< 0.1%
6 7
< 0.1%
7 2
 
< 0.1%
8 4
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
319 1
< 0.1%
164 1
< 0.1%
133 1
< 0.1%
123 1
< 0.1%
122 1
< 0.1%
108 1
< 0.1%
104 1
< 0.1%
97 1
< 0.1%
83 1
< 0.1%
74 1
< 0.1%

emi_plans_clicked
Real number (ℝ)

High correlation  Missing 

Distinct34
Distinct (%)55.7%
Missing239939
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean42.721311
Minimum1
Maximum246
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 MiB
2025-04-24T09:29:55.184441image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median10
Q366
95-th percentile204
Maximum246
Range245
Interquartile range (IQR)64

Descriptive statistics

Standard deviation63.348805
Coefficient of variation (CV)1.4828385
Kurtosis3.2181073
Mean42.721311
Median Absolute Deviation (MAD)9
Skewness1.9348378
Sum2606
Variance4013.071
MonotonicityNot monotonic
2025-04-24T09:29:55.226393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 11
 
< 0.1%
2 7
 
< 0.1%
3 4
 
< 0.1%
5 4
 
< 0.1%
52 2
 
< 0.1%
66 2
 
< 0.1%
25 2
 
< 0.1%
21 2
 
< 0.1%
10 2
 
< 0.1%
71 1
 
< 0.1%
Other values (24) 24
 
< 0.1%
(Missing) 239939
> 99.9%
ValueCountFrequency (%)
1 11
< 0.1%
2 7
< 0.1%
3 4
 
< 0.1%
4 1
 
< 0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
10 2
 
< 0.1%
11 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
246 1
< 0.1%
240 1
< 0.1%
232 1
< 0.1%
204 1
< 0.1%
152 1
< 0.1%
133 1
< 0.1%
124 1
< 0.1%
110 1
< 0.1%
107 1
< 0.1%
100 1
< 0.1%

fee_component_click
Categorical

High correlation  Missing 

Distinct3
Distinct (%)60.0%
Missing239995
Missing (%)> 99.9%
Memory size11.7 MiB
1.0
10.0
2.0

Length

Max length4
Median length3
Mean length3.2
Min length3

Characters and Unicode

Total characters16
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)40.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row10.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 3
 
< 0.1%
10.0 1
 
< 0.1%
2.0 1
 
< 0.1%
(Missing) 239995
> 99.9%

Length

2025-04-24T09:29:55.265627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:55.296653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3
60.0%
10.0 1
 
20.0%
2.0 1
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 6
37.5%
. 5
31.2%
1 4
25.0%
2 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11
68.8%
Other Punctuation 5
31.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6
54.5%
1 4
36.4%
2 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6
37.5%
. 5
31.2%
1 4
25.0%
2 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6
37.5%
. 5
31.2%
1 4
25.0%
2 1
 
6.2%

hiring_partners
Categorical

High correlation  Missing 

Distinct2
Distinct (%)22.2%
Missing239991
Missing (%)> 99.9%
Memory size11.7 MiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters27
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5
 
< 0.1%
2.0 4
 
< 0.1%
(Missing) 239991
> 99.9%

Length

2025-04-24T09:29:55.331732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:55.363857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5
55.6%
2.0 4
44.4%

Most occurring characters

ValueCountFrequency (%)
. 9
33.3%
0 9
33.3%
1 5
18.5%
2 4
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18
66.7%
Other Punctuation 9
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9
50.0%
1 5
27.8%
2 4
22.2%
Other Punctuation
ValueCountFrequency (%)
. 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9
33.3%
0 9
33.3%
1 5
18.5%
2 4
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9
33.3%
0 9
33.3%
1 5
18.5%
2 4
14.8%

homepage_upgrad_support_number_clicked
Unsupported

Missing  Rejected  Unsupported 

Missing240000
Missing (%)100.0%
Memory size11.7 MiB

industry_projects_case_studies
Unsupported

Missing  Rejected  Unsupported 

Missing240000
Missing (%)100.0%
Memory size11.7 MiB

live_chat_button_clicked
Categorical

High correlation  Missing 

Distinct4
Distinct (%)22.2%
Missing239982
Missing (%)> 99.9%
Memory size11.7 MiB
1.0
10 
2.0
3.0
4.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row4.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 10
 
< 0.1%
2.0 3
 
< 0.1%
3.0 3
 
< 0.1%
4.0 2
 
< 0.1%
(Missing) 239982
> 99.9%

Length

2025-04-24T09:29:55.400156image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:55.435876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10
55.6%
2.0 3
 
16.7%
3.0 3
 
16.7%
4.0 2
 
11.1%

Most occurring characters

ValueCountFrequency (%)
. 18
33.3%
0 18
33.3%
1 10
18.5%
2 3
 
5.6%
3 3
 
5.6%
4 2
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36
66.7%
Other Punctuation 18
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18
50.0%
1 10
27.8%
2 3
 
8.3%
3 3
 
8.3%
4 2
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 54
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 18
33.3%
0 18
33.3%
1 10
18.5%
2 3
 
5.6%
3 3
 
5.6%
4 2
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 18
33.3%
0 18
33.3%
1 10
18.5%
2 3
 
5.6%
3 3
 
5.6%
4 2
 
3.7%

payment_amount_toggle_mover
Unsupported

Missing  Rejected  Unsupported 

Missing240000
Missing (%)100.0%
Memory size11.7 MiB

placement_support
Categorical

High correlation  Missing 

Distinct2
Distinct (%)66.7%
Missing239997
Missing (%)> 99.9%
Memory size11.7 MiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st row1.0
2nd row2.0
3rd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
2.0 1
 
< 0.1%
(Missing) 239997
> 99.9%

Length

2025-04-24T09:29:55.474851image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:55.505412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
66.7%
2.0 1
33.3%

Most occurring characters

ValueCountFrequency (%)
. 3
33.3%
0 3
33.3%
1 2
22.2%
2 1
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6
66.7%
Other Punctuation 3
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3
50.0%
1 2
33.3%
2 1
 
16.7%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3
33.3%
0 3
33.3%
1 2
22.2%
2 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3
33.3%
0 3
33.3%
1 2
22.2%
2 1
 
11.1%

placement_support_banner_tab_clicked
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing239999
Missing (%)> 99.9%
Memory size11.7 MiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 239999
> 99.9%

Length

2025-04-24T09:29:55.538357image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:55.565878image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

program_structure
Unsupported

Missing  Rejected  Unsupported 

Missing240000
Missing (%)100.0%
Memory size11.7 MiB

programme_curriculum
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing239999
Missing (%)> 99.9%
Memory size11.7 MiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 239999
> 99.9%

Length

2025-04-24T09:29:55.595705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:55.624530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

programme_faculty
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing239999
Missing (%)> 99.9%
Memory size11.7 MiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 239999
> 99.9%

Length

2025-04-24T09:29:55.654633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:55.683602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%
Distinct1
Distinct (%)50.0%
Missing239998
Missing (%)> 99.9%
Memory size11.7 MiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
(Missing) 239998
> 99.9%

Length

2025-04-24T09:29:55.713187image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:55.744115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

shorts_entry_click
Categorical

High correlation  Missing 

Distinct4
Distinct (%)30.8%
Missing239987
Missing (%)> 99.9%
Memory size11.7 MiB
1.0
10 
32.0
 
1
9.0
 
1
4.0
 
1

Length

Max length4
Median length3
Mean length3.0769231
Min length3

Characters and Unicode

Total characters40
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)23.1%

Sample

1st row32.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 10
 
< 0.1%
32.0 1
 
< 0.1%
9.0 1
 
< 0.1%
4.0 1
 
< 0.1%
(Missing) 239987
> 99.9%

Length

2025-04-24T09:29:55.777840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:55.811854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10
76.9%
32.0 1
 
7.7%
9.0 1
 
7.7%
4.0 1
 
7.7%

Most occurring characters

ValueCountFrequency (%)
. 13
32.5%
0 13
32.5%
1 10
25.0%
3 1
 
2.5%
2 1
 
2.5%
9 1
 
2.5%
4 1
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27
67.5%
Other Punctuation 13
32.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13
48.1%
1 10
37.0%
3 1
 
3.7%
2 1
 
3.7%
9 1
 
3.7%
4 1
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 13
32.5%
0 13
32.5%
1 10
25.0%
3 1
 
2.5%
2 1
 
2.5%
9 1
 
2.5%
4 1
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 13
32.5%
0 13
32.5%
1 10
25.0%
3 1
 
2.5%
2 1
 
2.5%
9 1
 
2.5%
4 1
 
2.5%

social_referral_click
Categorical

High correlation  Missing 

Distinct4
Distinct (%)80.0%
Missing239995
Missing (%)> 99.9%
Memory size11.7 MiB
1.0
3.0
2.0
8.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)60.0%

Sample

1st row3.0
2nd row2.0
3rd row1.0
4th row8.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
3.0 1
 
< 0.1%
2.0 1
 
< 0.1%
8.0 1
 
< 0.1%
(Missing) 239995
> 99.9%

Length

2025-04-24T09:29:55.846517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:55.877834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
40.0%
3.0 1
20.0%
2.0 1
20.0%
8.0 1
20.0%

Most occurring characters

ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
1 2
 
13.3%
3 1
 
6.7%
2 1
 
6.7%
8 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5
50.0%
1 2
 
20.0%
3 1
 
10.0%
2 1
 
10.0%
8 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
1 2
 
13.3%
3 1
 
6.7%
2 1
 
6.7%
8 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
1 2
 
13.3%
3 1
 
6.7%
2 1
 
6.7%
8 1
 
6.7%

specialisation_tab_clicked
Real number (ℝ)

High correlation  Missing 

Distinct31
Distinct (%)70.5%
Missing239956
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean36.295455
Minimum1
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 MiB
2025-04-24T09:29:55.914570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14.75
median12
Q358.75
95-th percentile110.95
Maximum196
Range195
Interquartile range (IQR)54

Descriptive statistics

Standard deviation45.447429
Coefficient of variation (CV)1.2521521
Kurtosis2.6926345
Mean36.295455
Median Absolute Deviation (MAD)11
Skewness1.6664448
Sum1597
Variance2065.4688
MonotonicityNot monotonic
2025-04-24T09:29:56.038003image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 5
 
< 0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
4 3
 
< 0.1%
13 2
 
< 0.1%
6 2
 
< 0.1%
2 2
 
< 0.1%
88 1
 
< 0.1%
11 1
 
< 0.1%
98 1
 
< 0.1%
Other values (21) 21
 
< 0.1%
(Missing) 239956
> 99.9%
ValueCountFrequency (%)
1 5
< 0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 3
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 3
< 0.1%
9 3
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
196 1
< 0.1%
150 1
< 0.1%
112 1
< 0.1%
105 1
< 0.1%
102 1
< 0.1%
98 1
< 0.1%
88 1
< 0.1%
80 1
< 0.1%
75 1
< 0.1%
65 1
< 0.1%

specializations
Categorical

High correlation  Missing 

Distinct3
Distinct (%)75.0%
Missing239996
Missing (%)> 99.9%
Memory size11.7 MiB
1.0
3.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row1.0
2nd row3.0
3rd row2.0
4th row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
3.0 1
 
< 0.1%
2.0 1
 
< 0.1%
(Missing) 239996
> 99.9%

Length

2025-04-24T09:29:56.079663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:56.112558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
50.0%
3.0 1
25.0%
2.0 1
25.0%

Most occurring characters

ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 2
16.7%
3 1
 
8.3%
2 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8
66.7%
Other Punctuation 4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4
50.0%
1 2
25.0%
3 1
 
12.5%
2 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 2
16.7%
3 1
 
8.3%
2 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
1 2
16.7%
3 1
 
8.3%
2 1
 
8.3%

specilization_click
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)85.7%
Missing239993
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean5.2857143
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 MiB
2025-04-24T09:29:56.142851image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5
median5
Q37.5
95-th percentile11.8
Maximum13
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.4986771
Coefficient of variation (CV)0.85110106
Kurtosis-0.24622173
Mean5.2857143
Median Absolute Deviation (MAD)4
Skewness0.81435706
Sum37
Variance20.238095
MonotonicityNot monotonic
2025-04-24T09:29:56.176623image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 2
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
2 1
 
< 0.1%
9 1
 
< 0.1%
13 1
 
< 0.1%
(Missing) 239993
> 99.9%
ValueCountFrequency (%)
1 2
< 0.1%
2 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
9 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
13 1
< 0.1%
9 1
< 0.1%
6 1
< 0.1%
5 1
< 0.1%
2 1
< 0.1%
1 2
< 0.1%

syllabus
Real number (ℝ)

High correlation  Missing 

Distinct10
Distinct (%)34.5%
Missing239971
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean7.0689655
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 MiB
2025-04-24T09:29:56.208966image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile37
Maximum50
Range49
Interquartile range (IQR)3

Descriptive statistics

Standard deviation12.45257
Coefficient of variation (CV)1.7615831
Kurtosis5.8395134
Mean7.0689655
Median Absolute Deviation (MAD)1
Skewness2.5627208
Sum205
Variance155.0665
MonotonicityNot monotonic
2025-04-24T09:29:56.243001image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 10
 
< 0.1%
2 7
 
< 0.1%
4 4
 
< 0.1%
3 2
 
< 0.1%
34 1
 
< 0.1%
17 1
 
< 0.1%
6 1
 
< 0.1%
13 1
 
< 0.1%
50 1
 
< 0.1%
39 1
 
< 0.1%
(Missing) 239971
> 99.9%
ValueCountFrequency (%)
1 10
< 0.1%
2 7
< 0.1%
3 2
 
< 0.1%
4 4
 
< 0.1%
6 1
 
< 0.1%
13 1
 
< 0.1%
17 1
 
< 0.1%
34 1
 
< 0.1%
39 1
 
< 0.1%
50 1
 
< 0.1%
ValueCountFrequency (%)
50 1
 
< 0.1%
39 1
 
< 0.1%
34 1
 
< 0.1%
17 1
 
< 0.1%
13 1
 
< 0.1%
6 1
 
< 0.1%
4 4
 
< 0.1%
3 2
 
< 0.1%
2 7
< 0.1%
1 10
< 0.1%

syllabus_expand
Real number (ℝ)

High correlation  Missing 

Distinct49
Distinct (%)52.1%
Missing239906
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean83.446809
Minimum1
Maximum930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 MiB
2025-04-24T09:29:56.281831image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q371.5
95-th percentile390.85
Maximum930
Range929
Interquartile range (IQR)69.5

Descriptive statistics

Standard deviation188.41795
Coefficient of variation (CV)2.2579408
Kurtosis11.973591
Mean83.446809
Median Absolute Deviation (MAD)5
Skewness3.4202656
Sum7844
Variance35501.325
MonotonicityNot monotonic
2025-04-24T09:29:56.325480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2 15
 
< 0.1%
1 11
 
< 0.1%
4 7
 
< 0.1%
3 6
 
< 0.1%
6 5
 
< 0.1%
5 4
 
< 0.1%
8 3
 
< 0.1%
11 2
 
< 0.1%
42 1
 
< 0.1%
72 1
 
< 0.1%
Other values (39) 39
 
< 0.1%
(Missing) 239906
> 99.9%
ValueCountFrequency (%)
1 11
< 0.1%
2 15
< 0.1%
3 6
 
< 0.1%
4 7
< 0.1%
5 4
 
< 0.1%
6 5
 
< 0.1%
7 1
 
< 0.1%
8 3
 
< 0.1%
10 1
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
930 1
< 0.1%
928 1
< 0.1%
887 1
< 0.1%
739 1
< 0.1%
450 1
< 0.1%
359 1
< 0.1%
309 1
< 0.1%
275 1
< 0.1%
254 1
< 0.1%
242 1
< 0.1%

syllabus_submodule_expand
Real number (ℝ)

High correlation  Missing 

Distinct21
Distinct (%)50.0%
Missing239958
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean39.714286
Minimum1
Maximum457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 MiB
2025-04-24T09:29:56.362694image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.25
median5
Q321
95-th percentile179.5
Maximum457
Range456
Interquartile range (IQR)19.75

Descriptive statistics

Standard deviation92.791738
Coefficient of variation (CV)2.3364826
Kurtosis12.419075
Mean39.714286
Median Absolute Deviation (MAD)4
Skewness3.4586107
Sum1668
Variance8610.3066
MonotonicityNot monotonic
2025-04-24T09:29:56.398216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 11
 
< 0.1%
5 4
 
< 0.1%
2 3
 
< 0.1%
3 3
 
< 0.1%
14 2
 
< 0.1%
21 2
 
< 0.1%
4 2
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
56 1
 
< 0.1%
Other values (11) 11
 
< 0.1%
(Missing) 239958
> 99.9%
ValueCountFrequency (%)
1 11
< 0.1%
2 3
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%
5 4
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
14 2
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
457 1
< 0.1%
357 1
< 0.1%
181 1
< 0.1%
151 1
< 0.1%
118 1
< 0.1%
60 1
< 0.1%
56 1
< 0.1%
52 1
< 0.1%
38 1
< 0.1%
22 1
< 0.1%

tab_career_assistance
Real number (ℝ)

High correlation  Missing 

Distinct9
Distinct (%)36.0%
Missing239975
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean7.84
Minimum1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 MiB
2025-04-24T09:29:56.429301image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q37
95-th percentile26.4
Maximum57
Range56
Interquartile range (IQR)5

Descriptive statistics

Standard deviation12.853923
Coefficient of variation (CV)1.639531
Kurtosis8.6169289
Mean7.84
Median Absolute Deviation (MAD)1
Skewness2.7836573
Sum196
Variance165.22333
MonotonicityNot monotonic
2025-04-24T09:29:56.461609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 9
 
< 0.1%
1 6
 
< 0.1%
4 3
 
< 0.1%
24 2
 
< 0.1%
57 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
13 1
 
< 0.1%
27 1
 
< 0.1%
(Missing) 239975
> 99.9%
ValueCountFrequency (%)
1 6
< 0.1%
2 9
< 0.1%
4 3
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
13 1
 
< 0.1%
24 2
 
< 0.1%
27 1
 
< 0.1%
57 1
 
< 0.1%
ValueCountFrequency (%)
57 1
 
< 0.1%
27 1
 
< 0.1%
24 2
 
< 0.1%
13 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
4 3
 
< 0.1%
2 9
< 0.1%
1 6
< 0.1%

tab_job_opportunities
Real number (ℝ)

High correlation  Missing 

Distinct9
Distinct (%)47.4%
Missing239981
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean4.5789474
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 MiB
2025-04-24T09:29:56.494338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q38
95-th percentile12.2
Maximum14
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.6941546
Coefficient of variation (CV)1.0251602
Kurtosis-0.70220145
Mean4.5789474
Median Absolute Deviation (MAD)1
Skewness0.99553436
Sum87
Variance22.035088
MonotonicityNot monotonic
2025-04-24T09:29:56.528866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 8
 
< 0.1%
2 3
 
< 0.1%
12 2
 
< 0.1%
14 1
 
< 0.1%
3 1
 
< 0.1%
11 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
(Missing) 239981
> 99.9%
ValueCountFrequency (%)
1 8
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 2
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
12 2
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
3 1
 
< 0.1%
2 3
 
< 0.1%
1 8
< 0.1%

tab_student_support
Categorical

High correlation  Missing 

Distinct5
Distinct (%)62.5%
Missing239992
Missing (%)> 99.9%
Memory size11.7 MiB
1.0
2.0
6.0
3.0
8.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)37.5%

Sample

1st row1.0
2nd row6.0
3rd row1.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 3
 
< 0.1%
2.0 2
 
< 0.1%
6.0 1
 
< 0.1%
3.0 1
 
< 0.1%
8.0 1
 
< 0.1%
(Missing) 239992
> 99.9%

Length

2025-04-24T09:29:56.564748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:56.598779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3
37.5%
2.0 2
25.0%
6.0 1
 
12.5%
3.0 1
 
12.5%
8.0 1
 
12.5%

Most occurring characters

ValueCountFrequency (%)
. 8
33.3%
0 8
33.3%
1 3
 
12.5%
2 2
 
8.3%
6 1
 
4.2%
3 1
 
4.2%
8 1
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16
66.7%
Other Punctuation 8
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8
50.0%
1 3
 
18.8%
2 2
 
12.5%
6 1
 
6.2%
3 1
 
6.2%
8 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 8
33.3%
0 8
33.3%
1 3
 
12.5%
2 2
 
8.3%
6 1
 
4.2%
3 1
 
4.2%
8 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 8
33.3%
0 8
33.3%
1 3
 
12.5%
2 2
 
8.3%
6 1
 
4.2%
3 1
 
4.2%
8 1
 
4.2%

view_programs_page
Categorical

High correlation  Missing 

Distinct5
Distinct (%)41.7%
Missing239988
Missing (%)> 99.9%
Memory size11.7 MiB
2.0
3.0
1.0
4.0
11.0

Length

Max length4
Median length3
Mean length3.0833333
Min length3

Characters and Unicode

Total characters37
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)16.7%

Sample

1st row4.0
2nd row3.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 4
 
< 0.1%
3.0 3
 
< 0.1%
1.0 3
 
< 0.1%
4.0 1
 
< 0.1%
11.0 1
 
< 0.1%
(Missing) 239988
> 99.9%

Length

2025-04-24T09:29:56.640301image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:56.677858image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0 4
33.3%
3.0 3
25.0%
1.0 3
25.0%
4.0 1
 
8.3%
11.0 1
 
8.3%

Most occurring characters

ValueCountFrequency (%)
. 12
32.4%
0 12
32.4%
1 5
13.5%
2 4
 
10.8%
3 3
 
8.1%
4 1
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25
67.6%
Other Punctuation 12
32.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12
48.0%
1 5
20.0%
2 4
 
16.0%
3 3
 
12.0%
4 1
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 12
32.4%
0 12
32.4%
1 5
13.5%
2 4
 
10.8%
3 3
 
8.1%
4 1
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 12
32.4%
0 12
32.4%
1 5
13.5%
2 4
 
10.8%
3 3
 
8.1%
4 1
 
2.7%

whatsapp_chat_click
Categorical

High correlation  Missing 

Distinct5
Distinct (%)20.0%
Missing239975
Missing (%)> 99.9%
Memory size11.7 MiB
1.0
15 
2.0
3.0
16.0
 
1
13.0
 
1

Length

Max length4
Median length3
Mean length3.08
Min length3

Characters and Unicode

Total characters77
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)8.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 15
 
< 0.1%
2.0 5
 
< 0.1%
3.0 3
 
< 0.1%
16.0 1
 
< 0.1%
13.0 1
 
< 0.1%
(Missing) 239975
> 99.9%

Length

2025-04-24T09:29:56.719542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:56.754529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 15
60.0%
2.0 5
 
20.0%
3.0 3
 
12.0%
16.0 1
 
4.0%
13.0 1
 
4.0%

Most occurring characters

ValueCountFrequency (%)
. 25
32.5%
0 25
32.5%
1 17
22.1%
2 5
 
6.5%
3 4
 
5.2%
6 1
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52
67.5%
Other Punctuation 25
32.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25
48.1%
1 17
32.7%
2 5
 
9.6%
3 4
 
7.7%
6 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 77
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 25
32.5%
0 25
32.5%
1 17
22.1%
2 5
 
6.5%
3 4
 
5.2%
6 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 25
32.5%
0 25
32.5%
1 17
22.1%
2 5
 
6.5%
3 4
 
5.2%
6 1
 
1.3%

app_complete_flag
Categorical

High correlation  Uniform 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.7 MiB
0
120000 
1
120000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters240000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 120000
50.0%
1 120000
50.0%

Length

2025-04-24T09:29:56.794162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T09:29:56.826024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 120000
50.0%
1 120000
50.0%

Most occurring characters

ValueCountFrequency (%)
0 120000
50.0%
1 120000
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 240000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 120000
50.0%
1 120000
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 240000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 120000
50.0%
1 120000
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 240000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 120000
50.0%
1 120000
50.0%

Interactions

2025-04-24T09:29:52.004141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.030995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.385551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.720926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.063430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.416383image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.834634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.199725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.545206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.891210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.231210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.653478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:52.034486image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.060378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.414497image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.749421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.093291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.445893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.866735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.226020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.573437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.920391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.259269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.685416image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:52.063442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.089956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.443433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.779161image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.124184image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.473787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.896951image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.252088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.602400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.949022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.287351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.715021image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:52.091482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.118110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.470873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.806556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.152787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.502083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.927407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.281890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.629443image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.976404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.315972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.744978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:52.120178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.148607image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.502684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.835880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.183752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.531005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.959141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.309626image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.659723image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.006261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.344529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.775260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:52.148979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.176500image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.530019image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.862934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.212664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.558480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.988557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.339251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.687548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.033172image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.372232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.805860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:52.181212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.209946image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.561451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.894402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.245086image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.589853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.021915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.370643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.720009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.065005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.403600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.837665image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:52.207353image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.239493image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.585698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.924985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.273400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.623106image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.051644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.396519image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.749184image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.096268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.437070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.864948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:52.238869image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.269925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.613298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.953399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.302707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.651261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.081821image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.428648image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.777904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.124395image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.464128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.892906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:52.267649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.298451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.638765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.979401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.330138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.678153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.109508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.459291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.804595image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.149555image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.490532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.920315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:52.295950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.326605image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.665499image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.006152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.357018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.705053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.138621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.492043image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.831546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.175761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.516750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.947337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:52.325089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.354973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:48.692173image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.033268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.386150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:49.805609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.168246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.517812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:50.858825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.202352image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.623032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-24T09:29:51.975099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-04-24T09:29:56.866580image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
app_complete_flagcall_us_button_clickedcareer_assistancecareer_impactcareerscompaniesdownload_button_clickedemi_partner_clickemi_plans_clickedfee_component_clickfirst_platform_cfirst_utm_medium_chiring_partnerslive_chat_button_clickedplacement_supportreferred_leadshorts_entry_clicksocial_referral_clickspecialisation_tab_clickedspecializationsspecilization_clicksyllabussyllabus_expandsyllabus_submodule_expandtab_career_assistancetab_job_opportunitiestab_student_supporttotal_leads_dropppedview_programs_pagewhatsapp_chat_click
app_complete_flag1.0001.0000.7070.0000.3971.0000.2150.2600.1930.0000.4350.3760.0000.0000.0000.1680.0000.0000.0001.0000.0000.0000.2670.0000.3810.2520.2920.0240.0000.352
call_us_button_clicked1.0001.0000.0000.000NaN0.0001.0001.000NaN0.0001.0001.0000.000NaN0.0001.0000.0000.0000.0000.0000.000NaN1.0000.0000.0000.0000.0001.0000.0000.000
career_assistance0.7070.0001.0001.0001.000NaN0.7070.0001.0000.0000.0000.0001.000NaN0.0001.0000.0000.0000.7070.0000.0001.0000.0000.7071.0001.0001.0000.0000.0000.000
career_impact0.0000.0001.0001.0001.0001.0000.0001.0000.0000.0000.0000.0001.000NaNNaN1.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0000.0000.000
careers0.397NaN1.0001.0001.0001.000-0.0640.1920.1100.0000.4170.0000.0000.0000.0000.0000.0000.000-0.103NaNNaN0.0810.196-0.0950.154-0.1270.866-0.0410.0000.000
companies1.0000.000NaN1.0001.0001.0001.0001.0001.0000.0001.0001.000NaNNaN0.0001.0000.0000.0001.0000.0000.0001.0001.0001.0001.0001.0000.0001.0000.0000.000
download_button_clicked0.2151.0000.7070.000-0.0641.0001.0000.5060.6851.0000.1800.0680.4310.0001.0000.0000.000NaN0.3181.0000.6320.4440.7190.5900.1430.1700.4810.632NaN0.632
emi_partner_click0.2601.0000.0001.0000.1921.0000.5061.0000.6791.0000.0000.0000.0000.0001.0000.0001.000NaN0.6021.0000.4000.3040.7250.2880.7000.5930.4810.6751.0000.415
emi_plans_clicked0.193NaN1.0000.0000.1101.0000.6850.6791.0001.0000.1910.0000.2040.000NaN0.0000.000NaN0.5211.0000.3160.2480.8480.3560.5420.5580.0000.7351.0001.000
fee_component_click0.0000.0000.0000.0000.0000.0001.0001.0001.0001.0000.3330.3330.000NaN0.0000.000NaNNaNNaN0.000NaN0.0001.0001.000NaNNaN0.0000.8160.0001.000
first_platform_c0.4351.0000.0000.0000.4171.0000.1800.0000.1910.3331.0000.3680.0000.3940.0000.2250.0000.0000.0001.0000.0000.3060.0700.2090.5510.3830.1680.0130.0000.416
first_utm_medium_c0.3761.0000.0000.0000.0001.0000.0680.0000.0000.3330.3681.0000.6550.0001.0000.3210.0001.0000.0000.0000.7070.0000.0240.0000.0000.0000.2100.0180.0000.331
hiring_partners0.0000.0001.0001.0000.000NaN0.4310.0000.2040.0000.0000.6551.000NaN0.0001.0000.0000.0000.632NaN0.0000.0000.0000.0000.0001.0000.0000.4470.0000.000
live_chat_button_clicked0.000NaNNaNNaN0.000NaN0.0000.0000.000NaN0.3940.000NaN1.0000.0001.0000.000NaN0.0000.000NaN1.0000.0000.6920.0000.0001.0000.2510.0001.000
placement_support0.0000.0000.000NaN0.0000.0001.0001.000NaN0.0000.0001.0000.0000.0001.0001.0000.0000.0001.0000.0000.0001.0000.0001.000NaNNaN0.0000.000NaN0.000
referred_lead0.1681.0001.0001.0000.0001.0000.0000.0000.0000.0000.2250.3211.0001.0001.0001.0001.0000.0000.1381.0000.0000.0000.0000.0000.0001.0000.0000.0141.0000.000
shorts_entry_click0.0000.0000.0000.0000.0000.0000.0001.0000.000NaN0.0000.0000.0000.0000.0001.0001.000NaN0.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaN0.000
social_referral_click0.0000.0000.0000.0000.0000.000NaNNaNNaNNaN0.0001.0000.000NaN0.0000.000NaN1.000NaN0.000NaNNaNNaN0.000NaN0.0000.0001.000NaN0.000
specialisation_tab_clicked0.0000.0000.7070.000-0.1031.0000.3180.6020.521NaN0.0000.0000.6320.0001.0000.1380.000NaN1.0001.000NaN-0.1590.7240.3360.5110.3260.4330.484NaN1.000
specializations1.0000.0000.0000.000NaN0.0001.0001.0001.0000.0001.0000.000NaN0.0000.0001.0000.0000.0001.0001.0000.0000.0000.7071.000NaN0.0000.0001.0000.0000.000
specilization_click0.0000.0000.0000.000NaN0.0000.6320.4000.316NaN0.0000.7070.000NaN0.0000.0000.000NaNNaN0.0001.000NaN1.000-1.000NaNNaN0.0000.5820.0000.707
syllabus0.000NaN1.0000.0000.0811.0000.4440.3040.2480.0000.3060.0000.0001.0001.0000.0000.000NaN-0.1590.000NaN1.0000.473-0.102-0.1950.2450.6320.4890.000NaN
syllabus_expand0.2671.0000.0000.0000.1961.0000.7190.7250.8481.0000.0700.0240.0000.0000.0000.0000.000NaN0.7240.7071.0000.4731.0000.6550.6170.7870.0000.618NaN0.707
syllabus_submodule_expand0.0000.0000.7070.000-0.0951.0000.5900.2880.3561.0000.2090.0000.0000.6921.0000.0000.0000.0000.3361.000-1.000-0.1020.6551.0000.4530.5100.5770.343NaN1.000
tab_career_assistance0.3810.0001.0001.0000.1541.0000.1430.7000.542NaN0.5510.0000.0000.000NaN0.0000.000NaN0.511NaNNaN-0.1950.6170.4531.0000.7700.3330.195NaN1.000
tab_job_opportunities0.2520.0001.0001.000-0.1271.0000.1700.5930.558NaN0.3830.0001.0000.000NaN1.0000.0000.0000.3260.000NaN0.2450.7870.5100.7701.0001.0000.268NaNNaN
tab_student_support0.2920.0001.0001.0000.8660.0000.4810.4810.0000.0000.1680.2100.0001.0000.0000.0000.0000.0000.4330.0000.0000.6320.0000.5770.3331.0001.0000.0000.0000.000
total_leads_droppped0.0241.0000.0000.000-0.0411.0000.6320.6750.7350.8160.0130.0180.4470.2510.0000.0140.0001.0000.4841.0000.5820.4890.6180.3430.1950.2680.0001.0000.0000.705
view_programs_page0.0000.0000.0000.0000.0000.000NaN1.0001.0000.0000.0000.0000.0000.000NaN1.000NaNNaNNaN0.0000.0000.000NaNNaNNaNNaN0.0000.0001.0001.000
whatsapp_chat_click0.3520.0000.0000.0000.0000.0000.6320.4151.0001.0000.4160.3310.0001.0000.0000.0000.0000.0001.0000.0000.707NaN0.7071.0001.000NaN0.0000.7051.0001.000

Missing values

2025-04-24T09:29:52.413843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-24T09:29:52.843862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-24T09:29:53.618724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

created_datecity_mappedfirst_platform_cfirst_utm_medium_cfirst_utm_source_ctotal_leads_dropppedreferred_lead1_on_1_industry_mentorshipcall_us_button_clickedcareer_assistancecareer_coachcareer_impactcareerschat_clickedcompaniesdownload_button_clickeddownload_syllabusemi_partner_clickemi_plans_clickedfee_component_clickhiring_partnershomepage_upgrad_support_number_clickedindustry_projects_case_studieslive_chat_button_clickedpayment_amount_toggle_moverplacement_supportplacement_support_banner_tab_clickedprogram_structureprogramme_curriculumprogramme_facultyrequest_callback_on_instant_customer_support_cta_clickedshorts_entry_clicksocial_referral_clickspecialisation_tab_clickedspecializationsspecilization_clicksyllabussyllabus_expandsyllabus_submodule_expandtab_career_assistancetab_job_opportunitiestab_student_supportview_programs_pagewhatsapp_chat_clickapp_complete_flag
02021-08-13 10:44:09mumbaiLevel0Level0Level01.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0
12021-11-04 06:34:10ncrLevel1Level1Level11.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0
22021-07-29 14:01:00meerutLevel0Level2Level22.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0
32021-12-01 13:21:54ncrLevel1Level3Level31.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0
42021-12-10 07:22:51ahmedabadLevel2Level2Level21.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0
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created_datecity_mappedfirst_platform_cfirst_utm_medium_cfirst_utm_source_ctotal_leads_dropppedreferred_leadcall_us_button_clickedcareer_assistancecareer_impactcareerscompaniesdownload_button_clickedemi_partner_clickemi_plans_clickedfee_component_clickhiring_partnerslive_chat_button_clickedplacement_supportplacement_support_banner_tab_clickedprogramme_curriculumprogramme_facultyrequest_callback_on_instant_customer_support_cta_clickedshorts_entry_clicksocial_referral_clickspecialisation_tab_clickedspecializationsspecilization_clicksyllabussyllabus_expandsyllabus_submodule_expandtab_career_assistancetab_job_opportunitiestab_student_supportview_programs_pagewhatsapp_chat_clickapp_complete_flag# duplicates
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